Overview

Dataset statistics

Number of variables24
Number of observations4456615
Missing cells2732652
Missing cells (%)2.6%
Duplicate rows3299
Duplicate rows (%)0.1%
Total size in memory850.0 MiB
Average record size in memory200.0 B

Variable types

Numeric13
Categorical7
Text4

Alerts

activity_year has constant value ""Constant
Dataset has 3299 (0.1%) duplicate rowsDuplicates
debt_to_income_ratio is highly overall correlated with loan_outcome and 1 other fieldsHigh correlation
income is highly overall correlated with loan_amount and 1 other fieldsHigh correlation
lender_size is highly overall correlated with lender_type and 1 other fieldsHigh correlation
lender_type is highly overall correlated with lender_sizeHigh correlation
loan_amount is highly overall correlated with income and 1 other fieldsHigh correlation
loan_outcome is highly overall correlated with debt_to_income_ratioHigh correlation
mortgage_term is highly overall correlated with debt_to_income_ratio and 1 other fieldsHigh correlation
property_value_ratio is highly overall correlated with income and 1 other fieldsHigh correlation
mortgage_term is highly imbalanced (70.4%)Imbalance
property_value_ratio has 977860 (21.9%) missing valuesMissing
combined_loan_to_value_ratio has 793452 (17.8%) missing valuesMissing
white_population_pct has 167194 (3.8%) missing valuesMissing
metro_name has 295289 (6.6%) missing valuesMissing
state_code has 165896 (3.7%) missing valuesMissing
county_code has 165896 (3.7%) missing valuesMissing
census_tract has 167065 (3.7%) missing valuesMissing
income is highly skewed (γ1 = 524.6253903)Skewed
loan_amount is highly skewed (γ1 = 96.84012168)Skewed
property_value_ratio is highly skewed (γ1 = 1624.467788)Skewed
metro_size_percentile has 336394 (7.5%) zerosZeros

Reproduction

Analysis started2024-03-18 16:22:01.508046
Analysis finished2024-03-18 16:30:40.234958
Duration8 minutes and 38.73 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

race
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9787626
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.0 MiB
2024-03-18T11:30:40.382479image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median5
Q35
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.2518588
Coefficient of variation (CV)0.25143975
Kurtosis0.96360276
Mean4.9787626
Median Absolute Deviation (MAD)0
Skewness-0.81787609
Sum22188428
Variance1.5671505
MonotonicityNot monotonic
2024-03-18T11:30:40.507656image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5 2726798
61.2%
6 558572
 
12.5%
7 506449
 
11.4%
3 335051
 
7.5%
2 299736
 
6.7%
1 22266
 
0.5%
4 7743
 
0.2%
ValueCountFrequency (%)
1 22266
 
0.5%
2 299736
 
6.7%
3 335051
 
7.5%
4 7743
 
0.2%
5 2726798
61.2%
6 558572
 
12.5%
7 506449
 
11.4%
ValueCountFrequency (%)
7 506449
 
11.4%
6 558572
 
12.5%
5 2726798
61.2%
4 7743
 
0.2%
3 335051
 
7.5%
2 299736
 
6.7%
1 22266
 
0.5%

sex
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size68.0 MiB
1
2660576 
2
1517189 
3
276810 
6
 
2040

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4456615
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 2660576
59.7%
2 1517189
34.0%
3 276810
 
6.2%
6 2040
 
< 0.1%

Length

2024-03-18T11:30:40.588994image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T11:30:40.656662image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 2660576
59.7%
2 1517189
34.0%
3 276810
 
6.2%
6 2040
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 2660576
59.7%
2 1517189
34.0%
3 276810
 
6.2%
6 2040
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4456615
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2660576
59.7%
2 1517189
34.0%
3 276810
 
6.2%
6 2040
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 4456615
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2660576
59.7%
2 1517189
34.0%
3 276810
 
6.2%
6 2040
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4456615
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2660576
59.7%
2 1517189
34.0%
3 276810
 
6.2%
6 2040
 
< 0.1%

co_applicant
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size68.0 MiB
2
2502902 
1
1943046 
3
 
10667

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4456615
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 2502902
56.2%
1 1943046
43.6%
3 10667
 
0.2%

Length

2024-03-18T11:30:40.726503image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T11:30:40.783608image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 2502902
56.2%
1 1943046
43.6%
3 10667
 
0.2%

Most occurring characters

ValueCountFrequency (%)
2 2502902
56.2%
1 1943046
43.6%
3 10667
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4456615
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 2502902
56.2%
1 1943046
43.6%
3 10667
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 4456615
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 2502902
56.2%
1 1943046
43.6%
3 10667
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4456615
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 2502902
56.2%
1 1943046
43.6%
3 10667
 
0.2%

age
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2002174
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.0 MiB
2024-03-18T11:30:40.834110image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile6
Maximum8
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3636566
Coefficient of variation (CV)0.42611372
Kurtosis-0.15562359
Mean3.2002174
Median Absolute Deviation (MAD)1
Skewness0.65531119
Sum14262137
Variance1.8595592
MonotonicityNot monotonic
2024-03-18T11:30:40.900784image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2 1412786
31.7%
3 1200894
26.9%
4 782056
17.5%
5 508997
 
11.4%
1 243907
 
5.5%
6 241727
 
5.4%
7 63579
 
1.4%
8 2669
 
0.1%
ValueCountFrequency (%)
1 243907
 
5.5%
2 1412786
31.7%
3 1200894
26.9%
4 782056
17.5%
5 508997
 
11.4%
6 241727
 
5.4%
7 63579
 
1.4%
8 2669
 
0.1%
ValueCountFrequency (%)
8 2669
 
0.1%
7 63579
 
1.4%
6 241727
 
5.4%
5 508997
 
11.4%
4 782056
17.5%
3 1200894
26.9%
2 1412786
31.7%
1 243907
 
5.5%

income
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3863
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean114.75254
Minimum1
Maximum405346
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.0 MiB
2024-03-18T11:30:41.014518image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile32
Q155
median84
Q3130
95-th percentile275
Maximum405346
Range405345
Interquartile range (IQR)75

Descriptive statistics

Standard deviation518.73307
Coefficient of variation (CV)4.5204495
Kurtosis343346.1
Mean114.75254
Median Absolute Deviation (MAD)34
Skewness524.62539
Sum5.1140789 × 108
Variance269083.99
MonotonicityNot monotonic
2024-03-18T11:30:41.140890image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 56566
 
1.3%
50 52742
 
1.2%
52 49624
 
1.1%
65 48227
 
1.1%
55 47466
 
1.1%
62 46740
 
1.0%
70 46169
 
1.0%
48 45962
 
1.0%
75 45433
 
1.0%
42 45321
 
1.0%
Other values (3853) 3972365
89.1%
ValueCountFrequency (%)
1 824
< 0.1%
2 1264
< 0.1%
3 1704
< 0.1%
4 1730
< 0.1%
5 1635
< 0.1%
6 1460
< 0.1%
7 1314
< 0.1%
8 1209
< 0.1%
9 1320
< 0.1%
10 1273
< 0.1%
ValueCountFrequency (%)
405346 1
< 0.1%
400000 1
< 0.1%
365001 1
< 0.1%
360000 1
< 0.1%
323648 1
< 0.1%
250000 1
< 0.1%
235210 1
< 0.1%
200000 1
< 0.1%
189000 1
< 0.1%
167923 1
< 0.1%

loan_amount
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct733
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean291204.66
Minimum-1.292105 × 109
Maximum1.106255 × 109
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size68.0 MiB
2024-03-18T11:30:41.237127image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-1.292105 × 109
5-th percentile85000
Q1155000
median235000
Q3345000
95-th percentile665000
Maximum1.106255 × 109
Range2.39836 × 109
Interquartile range (IQR)190000

Descriptive statistics

Standard deviation1066865.1
Coefficient of variation (CV)3.6636267
Kurtosis915691.82
Mean291204.66
Median Absolute Deviation (MAD)90000
Skewness96.840122
Sum1.297787 × 1012
Variance1.1382012 × 1012
MonotonicityNot monotonic
2024-03-18T11:30:41.312650image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
205000 157354
 
3.5%
155000 151759
 
3.4%
165000 150651
 
3.4%
175000 146826
 
3.3%
185000 146274
 
3.3%
225000 145148
 
3.3%
195000 143153
 
3.2%
145000 139658
 
3.1%
215000 138049
 
3.1%
125000 131331
 
2.9%
Other values (723) 3006412
67.5%
ValueCountFrequency (%)
-1292105000 1
 
< 0.1%
5000 2220
 
< 0.1%
15000 3311
 
0.1%
25000 7301
 
0.2%
35000 13513
 
0.3%
45000 23214
 
0.5%
55000 41856
0.9%
65000 52451
1.2%
75000 65259
1.5%
85000 75361
1.7%
ValueCountFrequency (%)
1106255000 1
 
< 0.1%
899805000 1
 
< 0.1%
660005000 1
 
< 0.1%
568005000 1
 
< 0.1%
410475000 1
 
< 0.1%
396005000 1
 
< 0.1%
46025000 2
< 0.1%
42735000 3
< 0.1%
35005000 1
 
< 0.1%
29005000 1
 
< 0.1%

property_value_ratio
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct11833
Distinct (%)0.3%
Missing977860
Missing (%)21.9%
Infinite0
Infinite (%)0.0%
Mean1.3997054
Minimum0.008
Maximum12967.896
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.0 MiB
2024-03-18T11:30:41.395828image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0.008
5-th percentile0.556
Q10.885
median1.175
Q31.614
95-th percentile2.852
Maximum12967.896
Range12967.888
Interquartile range (IQR)0.729

Descriptive statistics

Standard deviation7.3176278
Coefficient of variation (CV)5.2279771
Kurtosis2843300.5
Mean1.3997054
Median Absolute Deviation (MAD)0.34
Skewness1624.4678
Sum4869232.1
Variance53.547676
MonotonicityNot monotonic
2024-03-18T11:30:41.477800image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.134 6952
 
0.2%
0.942 6870
 
0.2%
1.057 6788
 
0.2%
0.903 6130
 
0.1%
1.018 5989
 
0.1%
0.98 5860
 
0.1%
0.994 5780
 
0.1%
1.229 5717
 
0.1%
1.318 5553
 
0.1%
1.02 5492
 
0.1%
Other values (11823) 3417624
76.7%
(Missing) 977860
 
21.9%
ValueCountFrequency (%)
0.008 1
 
< 0.1%
0.009 1
 
< 0.1%
0.01 1
 
< 0.1%
0.011 2
 
< 0.1%
0.013 3
 
< 0.1%
0.014 4
 
< 0.1%
0.016 2
 
< 0.1%
0.017 17
< 0.1%
0.018 1
 
< 0.1%
0.019 10
< 0.1%
ValueCountFrequency (%)
12967.896 1
< 0.1%
3010.999 1
< 0.1%
1832.974 1
< 0.1%
646.663 1
< 0.1%
486.491 1
< 0.1%
454.295 1
< 0.1%
418.116 1
< 0.1%
390.051 1
< 0.1%
336.373 1
< 0.1%
319.423 1
< 0.1%

mortgage_term
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size68.0 MiB
1
4014636 
2
 
266458
4
 
139851
3
 
35670

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4456615
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
1 4014636
90.1%
2 266458
 
6.0%
4 139851
 
3.1%
3 35670
 
0.8%

Length

2024-03-18T11:30:41.546481image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T11:30:41.601124image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 4014636
90.1%
2 266458
 
6.0%
4 139851
 
3.1%
3 35670
 
0.8%

Most occurring characters

ValueCountFrequency (%)
1 4014636
90.1%
2 266458
 
6.0%
4 139851
 
3.1%
3 35670
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4456615
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4014636
90.1%
2 266458
 
6.0%
4 139851
 
3.1%
3 35670
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 4456615
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4014636
90.1%
2 266458
 
6.0%
4 139851
 
3.1%
3 35670
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4456615
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4014636
90.1%
2 266458
 
6.0%
4 139851
 
3.1%
3 35670
 
0.8%

credit_model
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.367178
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.0 MiB
2024-03-18T11:30:41.651391image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.2914832
Coefficient of variation (CV)0.68053522
Kurtosis-1.1140693
Mean3.367178
Median Absolute Deviation (MAD)2
Skewness0.67296203
Sum15006216
Variance5.2508953
MonotonicityNot monotonic
2024-03-18T11:30:41.707447image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 1145712
25.7%
7 1084625
24.3%
3 1083595
24.3%
2 913791
20.5%
5 174254
 
3.9%
6 49970
 
1.1%
4 4668
 
0.1%
ValueCountFrequency (%)
1 1145712
25.7%
2 913791
20.5%
3 1083595
24.3%
4 4668
 
0.1%
5 174254
 
3.9%
6 49970
 
1.1%
7 1084625
24.3%
ValueCountFrequency (%)
7 1084625
24.3%
6 49970
 
1.1%
5 174254
 
3.9%
4 4668
 
0.1%
3 1083595
24.3%
2 913791
20.5%
1 1145712
25.7%

debt_to_income_ratio
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7698675
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.0 MiB
2024-03-18T11:30:41.783113image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7651503
Coefficient of variation (CV)0.63726885
Kurtosis-0.74142445
Mean2.7698675
Median Absolute Deviation (MAD)1
Skewness0.74257001
Sum12344233
Variance3.1157554
MonotonicityNot monotonic
2024-03-18T11:30:41.846196image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 1441931
32.4%
2 937076
21.0%
3 864422
19.4%
6 728670
16.4%
4 359716
 
8.1%
5 124800
 
2.8%
ValueCountFrequency (%)
1 1441931
32.4%
2 937076
21.0%
3 864422
19.4%
4 359716
 
8.1%
5 124800
 
2.8%
6 728670
16.4%
ValueCountFrequency (%)
6 728670
16.4%
5 124800
 
2.8%
4 359716
 
8.1%
3 864422
19.4%
2 937076
21.0%
1 1441931
32.4%
Distinct96094
Distinct (%)2.6%
Missing793452
Missing (%)17.8%
Memory size68.0 MiB
2024-03-18T11:30:42.118993image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length16
Median length4
Mean length4.5862248
Min length3

Characters and Unicode

Total characters16800089
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30951 ?
Unique (%)0.8%

Sample

1st rowExempt
2nd rowExempt
3rd rowExempt
4th rowExempt
5th rowExempt
ValueCountFrequency (%)
80.0 607409
16.6%
96.5 495898
13.5%
95.0 482994
 
13.2%
90.0 244033
 
6.7%
97.0 237489
 
6.5%
exempt 124872
 
3.4%
85.0 81708
 
2.2%
100.0 76814
 
2.1%
75.0 63735
 
1.7%
70.0 29910
 
0.8%
Other values (96084) 1218301
33.3%
2024-03-18T11:30:42.448110image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 3538291
21.1%
0 3388392
20.2%
9 2378346
14.2%
5 1582202
9.4%
8 1330971
 
7.9%
6 1027400
 
6.1%
7 887581
 
5.3%
1 643855
 
3.8%
4 499906
 
3.0%
3 387643
 
2.3%
Other values (7) 1135502
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12512562
74.5%
Other Punctuation 3538291
 
21.1%
Lowercase Letter 624360
 
3.7%
Uppercase Letter 124876
 
0.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3388392
27.1%
9 2378346
19.0%
5 1582202
12.6%
8 1330971
 
10.6%
6 1027400
 
8.2%
7 887581
 
7.1%
1 643855
 
5.1%
4 499906
 
4.0%
3 387643
 
3.1%
2 386266
 
3.1%
Lowercase Letter
ValueCountFrequency (%)
m 124872
20.0%
p 124872
20.0%
t 124872
20.0%
e 124872
20.0%
x 124872
20.0%
Other Punctuation
ValueCountFrequency (%)
. 3538291
100.0%
Uppercase Letter
ValueCountFrequency (%)
E 124876
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 16050853
95.5%
Latin 749236
 
4.5%

Most frequent character per script

Common
ValueCountFrequency (%)
. 3538291
22.0%
0 3388392
21.1%
9 2378346
14.8%
5 1582202
9.9%
8 1330971
 
8.3%
6 1027400
 
6.4%
7 887581
 
5.5%
1 643855
 
4.0%
4 499906
 
3.1%
3 387643
 
2.4%
Latin
ValueCountFrequency (%)
E 124876
16.7%
m 124872
16.7%
p 124872
16.7%
t 124872
16.7%
e 124872
16.7%
x 124872
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16800089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 3538291
21.1%
0 3388392
20.2%
9 2378346
14.2%
5 1582202
9.4%
8 1330971
 
7.9%
6 1027400
 
6.1%
7 887581
 
5.3%
1 643855
 
3.8%
4 499906
 
3.0%
3 387643
 
2.3%
Other values (7) 1135502
 
6.8%

main_underwriter
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7321781
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.0 MiB
2024-03-18T11:30:42.514274image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.3011631
Coefficient of variation (CV)0.84224491
Kurtosis-0.6692483
Mean2.7321781
Median Absolute Deviation (MAD)0
Skewness1.0001502
Sum12176266
Variance5.2953517
MonotonicityNot monotonic
2024-03-18T11:30:42.563077image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 2259479
50.7%
7 714174
 
16.0%
2 568733
 
12.8%
3 530435
 
11.9%
6 270456
 
6.1%
5 112710
 
2.5%
4 628
 
< 0.1%
ValueCountFrequency (%)
1 2259479
50.7%
2 568733
 
12.8%
3 530435
 
11.9%
4 628
 
< 0.1%
5 112710
 
2.5%
6 270456
 
6.1%
7 714174
 
16.0%
ValueCountFrequency (%)
7 714174
 
16.0%
6 270456
 
6.1%
5 112710
 
2.5%
4 628
 
< 0.1%
3 530435
 
11.9%
2 568733
 
12.8%
1 2259479
50.7%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size68.0 MiB
3
1843814 
4
1699116 
2
634897 
5
 
172159
1
 
106629

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4456615
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 1843814
41.4%
4 1699116
38.1%
2 634897
 
14.2%
5 172159
 
3.9%
1 106629
 
2.4%

Length

2024-03-18T11:30:42.618020image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T11:30:42.670776image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
3 1843814
41.4%
4 1699116
38.1%
2 634897
 
14.2%
5 172159
 
3.9%
1 106629
 
2.4%

Most occurring characters

ValueCountFrequency (%)
3 1843814
41.4%
4 1699116
38.1%
2 634897
 
14.2%
5 172159
 
3.9%
1 106629
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4456615
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 1843814
41.4%
4 1699116
38.1%
2 634897
 
14.2%
5 172159
 
3.9%
1 106629
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
Common 4456615
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 1843814
41.4%
4 1699116
38.1%
2 634897
 
14.2%
5 172159
 
3.9%
1 106629
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4456615
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 1843814
41.4%
4 1699116
38.1%
2 634897
 
14.2%
5 172159
 
3.9%
1 106629
 
2.4%

lender_type
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size68.0 MiB
3
2599609 
1
1438863 
2
405485 
4
 
12658

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4456615
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
3 2599609
58.3%
1 1438863
32.3%
2 405485
 
9.1%
4 12658
 
0.3%

Length

2024-03-18T11:30:42.727781image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T11:30:42.779264image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
3 2599609
58.3%
1 1438863
32.3%
2 405485
 
9.1%
4 12658
 
0.3%

Most occurring characters

ValueCountFrequency (%)
3 2599609
58.3%
1 1438863
32.3%
2 405485
 
9.1%
4 12658
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4456615
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 2599609
58.3%
1 1438863
32.3%
2 405485
 
9.1%
4 12658
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 4456615
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 2599609
58.3%
1 1438863
32.3%
2 405485
 
9.1%
4 12658
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4456615
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 2599609
58.3%
1 1438863
32.3%
2 405485
 
9.1%
4 12658
 
0.3%

lender_size
Real number (ℝ)

HIGH CORRELATION 

Distinct1986
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean145933.16
Minimum1
Maximum1026755
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.0 MiB
2024-03-18T11:30:42.843631image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile700
Q15732
median25732
Q3153737
95-th percentile774905
Maximum1026755
Range1026754
Interquartile range (IQR)148005

Descriptive statistics

Standard deviation246597.39
Coefficient of variation (CV)1.6897969
Kurtosis4.3166477
Mean145933.16
Median Absolute Deviation (MAD)24474
Skewness2.2255056
Sum6.503679 × 1011
Variance6.0810274 × 1010
MonotonicityNot monotonic
2024-03-18T11:30:42.944886image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
774905 167281
 
3.8%
410835 165883
 
3.7%
1026755 149098
 
3.3%
198516 102803
 
2.3%
466552 89358
 
2.0%
527621 84340
 
1.9%
282102 77327
 
1.7%
257847 63864
 
1.4%
130400 61108
 
1.4%
119458 52230
 
1.2%
Other values (1976) 3443323
77.3%
ValueCountFrequency (%)
1 4
 
< 0.1%
2 4
 
< 0.1%
4 7
< 0.1%
5 6
 
< 0.1%
6 3
 
< 0.1%
7 7
< 0.1%
8 16
< 0.1%
9 5
 
< 0.1%
10 13
< 0.1%
11 9
< 0.1%
ValueCountFrequency (%)
1026755 149098
3.3%
774905 167281
3.8%
527621 84340
1.9%
466552 89358
2.0%
410835 165883
3.7%
380650 48475
 
1.1%
308884 24797
 
0.6%
302784 10059
 
0.2%
282102 77327
1.7%
257847 63864
 
1.4%

white_population_pct
Real number (ℝ)

MISSING 

Distinct70413
Distinct (%)1.6%
Missing167194
Missing (%)3.8%
Infinite0
Infinite (%)0.0%
Mean66.427039
Minimum0
Maximum100
Zeros5128
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size68.0 MiB
2024-03-18T11:30:43.023748image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12.976047
Q151.464264
median73.675337
Q386.661654
95-th percentile95.529826
Maximum100
Range100
Interquartile range (IQR)35.19739

Descriptive statistics

Standard deviation25.342034
Coefficient of variation (CV)0.38150178
Kurtosis-0.14374088
Mean66.427039
Median Absolute Deviation (MAD)15.538227
Skewness-0.89327985
Sum2.8493353 × 108
Variance642.21867
MonotonicityNot monotonic
2024-03-18T11:30:43.092710image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5128
 
0.1%
25.08549218 2287
 
0.1%
38.83451625 1920
 
< 0.1%
64.63691767 1769
 
< 0.1%
75.02294104 1574
 
< 0.1%
69.87282823 1573
 
< 0.1%
42.08020433 1520
 
< 0.1%
71.10172718 1483
 
< 0.1%
53.94154736 1479
 
< 0.1%
44.09115572 1297
 
< 0.1%
Other values (70403) 4269391
95.8%
(Missing) 167194
 
3.8%
ValueCountFrequency (%)
0 5128
0.1%
0.01163196464 18
 
< 0.1%
0.01282709082 5
 
< 0.1%
0.01891431814 94
 
< 0.1%
0.02134927412 2
 
< 0.1%
0.02161694769 2
 
< 0.1%
0.02297794118 18
 
< 0.1%
0.02919708029 4
 
< 0.1%
0.03082614057 26
 
< 0.1%
0.03355704698 6
 
< 0.1%
ValueCountFrequency (%)
100 1025
< 0.1%
99.96020692 10
 
< 0.1%
99.95645548 41
 
< 0.1%
99.93152248 24
 
< 0.1%
99.92146597 30
 
< 0.1%
99.92142483 6
 
< 0.1%
99.91421218 43
 
< 0.1%
99.90821478 16
 
< 0.1%
99.89059081 27
 
< 0.1%
99.88502443 53
 
< 0.1%

metro_name
Text

MISSING 

Distinct959
Distinct (%)< 0.1%
Missing295289
Missing (%)6.6%
Memory size68.0 MiB
2024-03-18T11:30:43.246232image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length49
Median length36
Mean length25.053283
Min length7

Characters and Unicode

Total characters104254880
Distinct characters62
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAmes, IA
2nd rowMason City, IA
3rd rowMason City, IA
4th rowAmes, IA
5th rowAlbert Lea, MN
ValueCountFrequency (%)
tx 390772
 
3.6%
ca 377632
 
3.4%
fl 351267
 
3.2%
ga 148022
 
1.4%
new 140225
 
1.3%
city 139081
 
1.3%
il 138121
 
1.3%
pa 133649
 
1.2%
az 128427
 
1.2%
mi 124043
 
1.1%
Other values (1079) 8880307
81.1%
2024-03-18T11:30:43.545052image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 7619290
 
7.3%
e 6824047
 
6.5%
6790220
 
6.5%
n 6615960
 
6.3%
o 6190369
 
5.9%
- 5758948
 
5.5%
r 5052660
 
4.8%
l 4584980
 
4.4%
i 4481362
 
4.3%
t 4251660
 
4.1%
Other values (52) 46085384
44.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 65598830
62.9%
Uppercase Letter 21735593
 
20.8%
Space Separator 6790220
 
6.5%
Dash Punctuation 5758948
 
5.5%
Other Punctuation 4371289
 
4.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 7619290
11.6%
e 6824047
10.4%
n 6615960
10.1%
o 6190369
9.4%
r 5052660
 
7.7%
l 4584980
 
7.0%
i 4481362
 
6.8%
t 4251660
 
6.5%
s 3600630
 
5.5%
d 2291205
 
3.5%
Other values (20) 14086667
21.5%
Uppercase Letter
ValueCountFrequency (%)
C 2419369
 
11.1%
A 2349171
 
10.8%
N 1591349
 
7.3%
S 1491949
 
6.9%
L 1389532
 
6.4%
M 1264273
 
5.8%
T 1012041
 
4.7%
P 967350
 
4.5%
W 948610
 
4.4%
B 943174
 
4.3%
Other values (16) 7358775
33.9%
Other Punctuation
ValueCountFrequency (%)
, 4161326
95.2%
. 185008
 
4.2%
/ 21386
 
0.5%
' 3569
 
0.1%
Space Separator
ValueCountFrequency (%)
6790220
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5758948
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 87334423
83.8%
Common 16920457
 
16.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 7619290
 
8.7%
e 6824047
 
7.8%
n 6615960
 
7.6%
o 6190369
 
7.1%
r 5052660
 
5.8%
l 4584980
 
5.2%
i 4481362
 
5.1%
t 4251660
 
4.9%
s 3600630
 
4.1%
C 2419369
 
2.8%
Other values (46) 35694096
40.9%
Common
ValueCountFrequency (%)
6790220
40.1%
- 5758948
34.0%
, 4161326
24.6%
. 185008
 
1.1%
/ 21386
 
0.1%
' 3569
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 104245168
> 99.9%
None 9712
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 7619290
 
7.3%
e 6824047
 
6.5%
6790220
 
6.5%
n 6615960
 
6.3%
o 6190369
 
5.9%
- 5758948
 
5.5%
r 5052660
 
4.8%
l 4584980
 
4.4%
i 4481362
 
4.3%
t 4251660
 
4.1%
Other values (48) 46075672
44.2%
None
ValueCountFrequency (%)
ó 8608
88.6%
ñ 716
 
7.4%
á 228
 
2.3%
ü 160
 
1.6%

metro_size_percentile
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.189303
Minimum0
Maximum111
Zeros336394
Zeros (%)7.5%
Negative0
Negative (%)0.0%
Memory size68.0 MiB
2024-03-18T11:30:43.631885image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median9
Q39
95-th percentile111
Maximum111
Range111
Interquartile range (IQR)2

Descriptive statistics

Standard deviation35.385262
Coefficient of variation (CV)1.5946991
Kurtosis1.5245638
Mean22.189303
Median Absolute Deviation (MAD)1
Skewness1.8568124
Sum98889179
Variance1252.1168
MonotonicityNot monotonic
2024-03-18T11:30:43.710854image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
9 1547716
34.7%
8 709136
15.9%
99 466958
 
10.5%
7 421596
 
9.5%
0 336394
 
7.5%
111 242745
 
5.4%
6 235461
 
5.3%
5 162238
 
3.6%
4 115719
 
2.6%
3 93262
 
2.1%
Other values (2) 125390
 
2.8%
ValueCountFrequency (%)
0 336394
 
7.5%
1 55460
 
1.2%
2 69930
 
1.6%
3 93262
 
2.1%
4 115719
 
2.6%
5 162238
 
3.6%
6 235461
 
5.3%
7 421596
 
9.5%
8 709136
15.9%
9 1547716
34.7%
ValueCountFrequency (%)
111 242745
 
5.4%
99 466958
 
10.5%
9 1547716
34.7%
8 709136
15.9%
7 421596
 
9.5%
6 235461
 
5.3%
5 162238
 
3.6%
4 115719
 
2.6%
3 93262
 
2.1%
2 69930
 
1.6%

state_code
Real number (ℝ)

MISSING 

Distinct52
Distinct (%)< 0.1%
Missing165896
Missing (%)3.7%
Infinite0
Infinite (%)0.0%
Mean27.964869
Minimum1
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.0 MiB
2024-03-18T11:30:43.782702image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q112
median27
Q342
95-th percentile53
Maximum72
Range71
Interquartile range (IQR)30

Descriptive statistics

Standard deviation16.330678
Coefficient of variation (CV)0.5839712
Kurtosis-1.2988164
Mean27.964869
Median Absolute Deviation (MAD)15
Skewness0.054807516
Sum1.1998939 × 108
Variance266.69104
MonotonicityNot monotonic
2024-03-18T11:30:43.877127image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48 401535
 
9.0%
6 380461
 
8.5%
12 353025
 
7.9%
17 164128
 
3.7%
13 162564
 
3.6%
39 160341
 
3.6%
36 155299
 
3.5%
37 152544
 
3.4%
42 148457
 
3.3%
26 132121
 
3.0%
Other values (42) 2080244
46.7%
(Missing) 165896
 
3.7%
ValueCountFrequency (%)
1 57102
 
1.3%
2 7281
 
0.2%
4 128654
 
2.9%
5 32172
 
0.7%
6 380461
8.5%
8 110776
 
2.5%
9 46330
 
1.0%
10 13871
 
0.3%
11 9377
 
0.2%
12 353025
7.9%
ValueCountFrequency (%)
72 10673
 
0.2%
56 6667
 
0.1%
55 77930
 
1.7%
54 14906
 
0.3%
53 118035
 
2.6%
51 113960
 
2.6%
50 6476
 
0.1%
49 62389
 
1.4%
48 401535
9.0%
47 97799
 
2.2%

county_code
Real number (ℝ)

MISSING 

Distinct322
Distinct (%)< 0.1%
Missing165896
Missing (%)3.7%
Infinite0
Infinite (%)0.0%
Mean87.772382
Minimum1
Maximum840
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.0 MiB
2024-03-18T11:30:44.072071image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q129
median65
Q3111
95-th percentile221
Maximum840
Range839
Interquartile range (IQR)82

Descriptive statistics

Standard deviation99.219352
Coefficient of variation (CV)1.1304165
Kurtosis14.040813
Mean87.772382
Median Absolute Deviation (MAD)40
Skewness3.1935013
Sum3.7660662 × 108
Variance9844.4798
MonotonicityNot monotonic
2024-03-18T11:30:44.667080image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 152295
 
3.4%
31 140248
 
3.1%
3 138386
 
3.1%
37 109257
 
2.5%
1 102351
 
2.3%
59 87640
 
2.0%
5 85793
 
1.9%
29 78275
 
1.8%
35 76930
 
1.7%
11 75116
 
1.7%
Other values (312) 3244428
72.8%
(Missing) 165896
 
3.7%
ValueCountFrequency (%)
1 102351
2.3%
3 138386
3.1%
5 85793
1.9%
6 54
 
< 0.1%
7 43918
 
1.0%
9 54394
 
1.2%
11 75116
1.7%
12 63
 
< 0.1%
13 152295
3.4%
14 1653
 
< 0.1%
ValueCountFrequency (%)
840 282
 
< 0.1%
830 138
 
< 0.1%
820 442
 
< 0.1%
810 5366
0.1%
800 1196
 
< 0.1%
790 387
 
< 0.1%
775 324
 
< 0.1%
770 1310
 
< 0.1%
760 2943
0.1%
750 79
 
< 0.1%

census_tract
Text

MISSING 

Distinct71951
Distinct (%)1.7%
Missing167065
Missing (%)3.7%
Memory size68.0 MiB
2024-03-18T11:30:44.978633image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.999996
Min length2

Characters and Unicode

Total characters47185032
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique783 ?
Unique (%)< 0.1%

Sample

1st row19081270100
2nd row19081270200
3rd row19169010600
4th row19081270100
5th row19081270100
ValueCountFrequency (%)
48157672900 2287
 
0.1%
48201542900 1920
 
< 0.1%
48157673200 1769
 
< 0.1%
48085030305 1574
 
< 0.1%
48085030203 1573
 
< 0.1%
48157673101 1520
 
< 0.1%
48439114103 1483
 
< 0.1%
48157673400 1479
 
< 0.1%
48201543002 1297
 
< 0.1%
49035113107 1291
 
< 0.1%
Other values (71941) 4273357
99.6%
2024-03-18T11:30:45.316232image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 15876296
33.6%
1 7493303
15.9%
3 4337380
 
9.2%
2 4260146
 
9.0%
5 3144274
 
6.7%
4 3086982
 
6.5%
7 2519965
 
5.3%
9 2420755
 
5.1%
6 2190757
 
4.6%
8 1855170
 
3.9%
Other values (2) 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 47185028
> 99.9%
Uppercase Letter 2
 
< 0.1%
Lowercase Letter 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15876296
33.6%
1 7493303
15.9%
3 4337380
 
9.2%
2 4260146
 
9.0%
5 3144274
 
6.7%
4 3086982
 
6.5%
7 2519965
 
5.3%
9 2420755
 
5.1%
6 2190757
 
4.6%
8 1855170
 
3.9%
Uppercase Letter
ValueCountFrequency (%)
N 2
100.0%
Lowercase Letter
ValueCountFrequency (%)
a 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 47185028
> 99.9%
Latin 4
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15876296
33.6%
1 7493303
15.9%
3 4337380
 
9.2%
2 4260146
 
9.0%
5 3144274
 
6.7%
4 3086982
 
6.5%
7 2519965
 
5.3%
9 2420755
 
5.1%
6 2190757
 
4.6%
8 1855170
 
3.9%
Latin
ValueCountFrequency (%)
N 2
50.0%
a 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 47185032
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15876296
33.6%
1 7493303
15.9%
3 4337380
 
9.2%
2 4260146
 
9.0%
5 3144274
 
6.7%
4 3086982
 
6.5%
7 2519965
 
5.3%
9 2420755
 
5.1%
6 2190757
 
4.6%
8 1855170
 
3.9%
Other values (2) 4
 
< 0.1%

activity_year
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size68.0 MiB
2019
4456615 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters17826460
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2019 4456615
100.0%

Length

2024-03-18T11:30:45.397240image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T11:30:45.444619image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2019 4456615
100.0%

Most occurring characters

ValueCountFrequency (%)
2 4456615
25.0%
0 4456615
25.0%
1 4456615
25.0%
9 4456615
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17826460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 4456615
25.0%
0 4456615
25.0%
1 4456615
25.0%
9 4456615
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 17826460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 4456615
25.0%
0 4456615
25.0%
1 4456615
25.0%
9 4456615
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17826460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 4456615
25.0%
0 4456615
25.0%
1 4456615
25.0%
9 4456615
25.0%

loan_outcome
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size68.0 MiB
1
3215722 
4
922723 
3
 
318170

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4456615
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 3215722
72.2%
4 922723
 
20.7%
3 318170
 
7.1%

Length

2024-03-18T11:30:45.493375image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T11:30:45.542176image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 3215722
72.2%
4 922723
 
20.7%
3 318170
 
7.1%

Most occurring characters

ValueCountFrequency (%)
1 3215722
72.2%
4 922723
 
20.7%
3 318170
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4456615
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3215722
72.2%
4 922723
 
20.7%
3 318170
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
Common 4456615
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3215722
72.2%
4 922723
 
20.7%
3 318170
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4456615
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3215722
72.2%
4 922723
 
20.7%
3 318170
 
7.1%
Distinct5144
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size68.0 MiB
2024-03-18T11:30:45.755931image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters89132300
Distinct characters36
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)< 0.1%

Sample

1st row25490003YGASV5ENH153
2nd row25490003YGASV5ENH153
3rd row25490003YGASV5ENH153
4th row25490003YGASV5ENH153
5th row25490003YGASV5ENH153
ValueCountFrequency (%)
549300fgxn1k3hlb1r50 167281
 
3.8%
549300hw662mn1wu8550 165883
 
3.7%
kb1h1dsprfmymcufxt09 149098
 
3.3%
549300mgpzblqdil7538 102803
 
2.3%
b4tydeb6gkmzo031mb27 89358
 
2.0%
7h6glxdrugqfu57rne97 84340
 
1.9%
549300j7xkt2bi5wx213 77327
 
1.7%
549300ag64nhilb7zp05 63864
 
1.4%
549300u3721pjgqzyy68 61108
 
1.4%
549300aq3t62gxdu7d76 52230
 
1.2%
Other values (5134) 3443323
77.3%
2024-03-18T11:30:46.036743image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 9786828
 
11.0%
5 6314133
 
7.1%
3 5826707
 
6.5%
4 5696358
 
6.4%
9 5046116
 
5.7%
1 3231566
 
3.6%
2 2809968
 
3.2%
7 2644075
 
3.0%
6 2573103
 
2.9%
8 2147321
 
2.4%
Other values (26) 43056125
48.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46076175
51.7%
Uppercase Letter 43056125
48.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 2069572
 
4.8%
H 2026636
 
4.7%
D 2009257
 
4.7%
M 2002468
 
4.7%
N 1970014
 
4.6%
R 1942877
 
4.5%
S 1826433
 
4.2%
W 1817562
 
4.2%
G 1810619
 
4.2%
L 1797071
 
4.2%
Other values (16) 23783616
55.2%
Decimal Number
ValueCountFrequency (%)
0 9786828
21.2%
5 6314133
13.7%
3 5826707
12.6%
4 5696358
12.4%
9 5046116
11.0%
1 3231566
 
7.0%
2 2809968
 
6.1%
7 2644075
 
5.7%
6 2573103
 
5.6%
8 2147321
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Common 46076175
51.7%
Latin 43056125
48.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 2069572
 
4.8%
H 2026636
 
4.7%
D 2009257
 
4.7%
M 2002468
 
4.7%
N 1970014
 
4.6%
R 1942877
 
4.5%
S 1826433
 
4.2%
W 1817562
 
4.2%
G 1810619
 
4.2%
L 1797071
 
4.2%
Other values (16) 23783616
55.2%
Common
ValueCountFrequency (%)
0 9786828
21.2%
5 6314133
13.7%
3 5826707
12.6%
4 5696358
12.4%
9 5046116
11.0%
1 3231566
 
7.0%
2 2809968
 
6.1%
7 2644075
 
5.7%
6 2573103
 
5.6%
8 2147321
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 89132300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9786828
 
11.0%
5 6314133
 
7.1%
3 5826707
 
6.5%
4 5696358
 
6.4%
9 5046116
 
5.7%
1 3231566
 
3.6%
2 2809968
 
3.2%
7 2644075
 
3.0%
6 2573103
 
2.9%
8 2147321
 
2.4%
Other values (26) 43056125
48.3%

Interactions

2024-03-18T11:29:52.232751image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:25:59.894527image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:26:18.977126image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:26:37.626217image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:26:50.088075image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:27:02.841243image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:27:14.784892image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:27:34.032870image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:27:53.038579image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:28:12.264879image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:28:58.549660image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:29:12.337207image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:29:31.728875image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:29:53.873570image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:26:01.220631image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:26:20.158355image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:26:38.400940image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:26:50.868911image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:27:03.506719image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:27:16.082609image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:27:35.330706image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:27:54.317991image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:28:15.782831image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:28:59.370028image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:29:13.645053image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:29:33.098187image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:29:55.210691image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:26:02.032563image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:26:20.955883image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:26:38.663563image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:26:51.145104image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:27:03.764333image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:27:16.896031image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:27:36.137978image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:27:55.115764image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:28:18.710692image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:28:59.698499image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:29:14.478014image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:29:34.058986image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:29:56.384251image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:26:02.831683image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:26:21.770420image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:26:38.945905image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:26:51.408379image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:27:04.038164image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:27:17.715624image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:27:36.952703image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:27:55.914715image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:28:21.634141image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:29:00.051892image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:29:15.316363image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:29:35.166816image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:29:57.409854image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:26:03.517695image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:26:22.469449image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:26:39.203651image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:26:51.670878image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:27:04.283146image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:27:18.420741image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:27:37.643864image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:27:56.620098image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:28:24.452027image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:29:00.332528image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:29:16.028474image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:29:35.983603image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:29:59.053029image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:26:04.800096image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:26:23.781636image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:26:39.981554image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:26:52.472834image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:27:04.952625image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:27:19.606405image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:27:38.956870image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:27:57.982345image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:28:27.770312image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:29:01.186635image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:29:17.346724image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:29:37.359966image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:30:00.814045image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:26:06.073505image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:26:25.082109image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:26:40.763468image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:26:53.248677image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:27:05.610254image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:27:20.909673image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:27:40.140176image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:27:59.271540image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:28:31.157325image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:29:02.094217image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:29:18.659875image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:29:38.731357image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:30:02.555094image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:26:07.330587image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:26:26.369896image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:26:41.527463image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:26:54.037296image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:27:06.272109image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:27:22.196911image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:27:41.415183image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:28:00.418076image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:28:34.769516image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:29:02.923414image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:29:19.949231image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:29:40.094753image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:30:04.226328image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:26:08.621151image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:26:27.691257image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:26:42.328771image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:26:54.885694image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:27:06.962861image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:27:23.530921image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:27:42.733585image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:28:01.781952image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:28:38.083976image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:29:03.772611image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:29:21.271409image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:29:41.491768image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:30:09.127001image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:26:13.017646image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:26:32.119343image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:26:46.234952image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:26:58.973129image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:27:10.648166image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:27:27.992130image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:27:47.163269image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:28:06.389821image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:28:44.490579image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:29:07.869094image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:29:25.893994image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:29:46.182135image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:30:10.770227image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:26:14.310582image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:26:33.432709image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:26:47.013137image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:26:59.772600image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:27:11.323378image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:27:29.304918image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:27:48.470608image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:28:07.695334image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:28:48.076549image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:29:08.691058image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:29:27.096743image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:29:47.558428image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:30:12.456169image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:26:15.626411image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:26:34.773535image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:26:47.856854image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:27:00.630970image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:27:12.037494image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:27:30.648814image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:27:49.814016image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:28:09.034001image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:28:51.539092image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:29:09.560655image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:29:28.449259image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:29:48.851565image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:30:14.091392image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:26:17.004906image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:26:36.180957image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:26:48.759594image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:27:01.625570image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:27:12.943855image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:27:32.055123image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:27:51.218917image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:28:10.437279image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:28:55.068084image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:29:10.495317image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:29:29.863852image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T11:29:50.327480image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Correlations

2024-03-18T11:30:46.108667image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ageco_applicantcounty_codecredit_modeldebt_to_income_ratioincomelender_sizelender_typeloan_amountloan_outcomemain_underwritermetro_size_percentilemortgage_termproperty_value_ratioracesexstate_codetract_to_metro_income_percentagewhite_population_pct
age1.0000.047-0.0140.0130.0260.1070.0160.0360.0390.0330.019-0.0060.0730.145-0.0120.019-0.0470.0600.039
co_applicant0.0471.0000.022-0.0860.054-0.3650.0120.042-0.2210.030-0.002-0.0030.039-0.232-0.0250.096-0.0050.079-0.082
county_code-0.0140.0221.000-0.005-0.009-0.056-0.0380.103-0.1440.0380.0040.0250.0680.062-0.0050.0330.2130.125-0.017
credit_model0.013-0.086-0.0051.0000.4420.025-0.1100.0850.0050.4630.172-0.0040.1760.0070.0020.021-0.0000.0360.002
debt_to_income_ratio0.0260.054-0.0090.4421.000-0.179-0.0430.1450.0120.6030.1680.0110.547-0.0980.0210.041-0.0290.059-0.089
income0.107-0.365-0.0560.025-0.1791.0000.0770.0000.7120.0020.0320.1460.0000.563-0.0260.005-0.0380.0020.044
lender_size0.0160.012-0.038-0.110-0.0430.0771.0000.9460.1390.2300.0150.1230.5380.0150.0260.161-0.0890.289-0.104
lender_type0.0360.0420.1030.0850.1450.0000.9461.0000.0070.077-0.2230.0690.144-0.1200.0570.028-0.0410.142-0.141
loan_amount0.039-0.221-0.1440.0050.0120.7120.1390.0071.0000.0010.0180.2000.0010.579-0.0160.000-0.1130.000-0.079
loan_outcome0.0330.0300.0380.4630.6030.0020.2300.0770.0011.0000.167-0.0770.024-0.0440.0070.029-0.0110.227-0.054
main_underwriter0.019-0.0020.0040.1720.1680.0320.015-0.2230.0180.1671.000-0.0060.2440.031-0.0120.032-0.0070.0980.006
metro_size_percentile-0.006-0.0030.025-0.0040.0110.1460.1230.0690.200-0.077-0.0061.0000.085-0.006-0.0020.018-0.0390.373-0.229
mortgage_term0.0730.0390.0680.1760.5470.0000.5380.1440.0010.0240.2440.0851.0000.090-0.0230.0280.0520.0410.123
property_value_ratio0.145-0.2320.0620.007-0.0980.5630.015-0.1200.579-0.0440.031-0.0060.0901.000-0.0300.0000.0380.0000.201
race-0.012-0.025-0.0050.0020.021-0.0260.0260.057-0.0160.007-0.012-0.002-0.023-0.0301.0000.389-0.0160.101-0.022
sex0.0190.0960.0330.0210.0410.0050.1610.0280.0000.0290.0320.0180.0280.0000.3891.000-0.0060.039-0.047
state_code-0.047-0.0050.213-0.000-0.029-0.038-0.089-0.041-0.113-0.011-0.007-0.0390.0520.038-0.016-0.0061.0000.0880.142
tract_to_metro_income_percentage0.0600.0790.1250.0360.0590.0020.2890.1420.0000.2270.0980.3730.0410.0000.1010.0390.0881.0000.265
white_population_pct0.039-0.082-0.0170.002-0.0890.044-0.104-0.141-0.079-0.0540.006-0.2290.1230.201-0.022-0.0470.1420.2651.000

Missing values

2024-03-18T11:30:16.730656image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-18T11:30:21.422302image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

racesexco_applicantageincomeloan_amountproperty_value_ratiomortgage_termcredit_modeldebt_to_income_ratiocombined_loan_to_value_ratiomain_underwritertract_to_metro_income_percentagelender_typelender_sizewhite_population_pctmetro_namemetro_size_percentilestate_codecounty_codecensus_tractactivity_yearloan_outcomelender_id
222522223.025000NaN475Exempt73110794.487578NaN00019081190812701002019125490003YGASV5ENH153
230512242.085000NaN475Exempt73110793.845535NaN00019081190812702002019125490003YGASV5ENH153
2345112125.095000NaN475Exempt73110796.340348Ames, IA119169191690106002019125490003YGASV5ENH153
245512134.075000NaN475Exempt73110794.487578NaN00019081190812701002019125490003YGASV5ENH153
260522237.0145000NaN475Exempt73110794.487578NaN00019081190812701002019125490003YGASV5ENH153
261521457.075000NaN475Exempt73110792.326835NaN00019081190812703002019125490003YGASV5ENH153
269512127.065000NaN475Exempt73110793.845535NaN00019081190812702002019125490003YGASV5ENH153
276512132.075000NaN475Exempt73110794.487578NaN00019081190812701002019125490003YGASV5ENH153
281512235.025000NaN475Exempt73110793.845535NaN00019081190812702002019125490003YGASV5ENH153
2895113123.0175000NaN475Exempt73110792.326835NaN00019081190812703002019125490003YGASV5ENH153
racesexco_applicantageincomeloan_amountproperty_value_ratiomortgage_termcredit_modeldebt_to_income_ratiocombined_loan_to_value_ratiomain_underwritertract_to_metro_income_percentagelender_typelender_sizewhite_population_pctmetro_namemetro_size_percentilestate_codecounty_codecensus_tractactivity_yearloan_outcomelender_id
175453732214133.0655000NaN176NaN743189478.854426Cambridge-Newton-Framingham, MA9250172501736710020194549300L0OVX5O63S8C68
1754537673252628.01985000NaN176NaN743189482.484383Boston, MA9250252502506060020194549300L0OVX5O63S8C68
175453837124947.014950007.59617146.123743189479.474940Bridgeport-Stamford-Norwalk, CT8090010900101110020191549300L0OVX5O63S8C68
175453865215196.03750000.92917180.0133189480.891304Cambridge-Newton-Framingham, MA9250172501731710220191549300L0OVX5O63S8C68
17545388731568.03150001.61813365.235743189489.135066Providence-Warwick, RI-MA8250052500563170020191549300L0OVX5O63S8C68
175453905215196.0395000NaN176NaN133189467.825622Cambridge-Newton-Framingham, MA9250172501733230020194549300L0OVX5O63S8C68
175453925113365.08650002.28317380.0743189493.231994Boston, MA9250212502140910220191549300L0OVX5O63S8C68
17545394732425.0850000.33913390.0213189448.053528Worcester, MA-CT8250272502771070020191549300L0OVX5O63S8C68
175453955212318.06850003.04713195.0743189492.470277Worcester, MA-CT8250272502771510020191549300L0OVX5O63S8C68
175453977212315.01645000NaN176NaN743189464.553795Boston, MA9250252502507080020194549300L0OVX5O63S8C68

Duplicate rows

Most frequently occurring

racesexco_applicantageincomeloan_amountproperty_value_ratiomortgage_termcredit_modeldebt_to_income_ratiocombined_loan_to_value_ratiomain_underwritertract_to_metro_income_percentagelender_typelender_sizewhite_population_pctmetro_namemetro_size_percentilestate_codecounty_codecensus_tractactivity_yearloan_outcomelender_id# duplicates
32267323102.0305000NaN176NaN7139947215.104446Atlanta-Sandy Springs-Alpharetta, GA99131211312101180020194549300VORTI31GZTJL5337
32277323102.0305000NaN476NaN7139947215.104446Atlanta-Sandy Springs-Alpharetta, GA99131211312101180020194549300VORTI31GZTJL5326
32257323102.0305000NaN176NaN1139947215.104446Atlanta-Sandy Springs-Alpharetta, GA99131211312101180020194549300VORTI31GZTJL5322
21755223102.0305000NaN176NaN7139947215.104446Atlanta-Sandy Springs-Alpharetta, GA99131211312101180020194549300VORTI31GZTJL539
544322390.0355000NaN176NaN7132821025.413345Oakland-Berkeley-Livermore, CA9060010600140880020194549300J7XKT2BI5WX2134
15255123600.01525000NaN276NaN7412596888.379205Dallas-Plano-Irving, TX9481134811301300420194FU7RSW4CQQY98A2O7J664
29937124139.0415000NaN176NaN7431408384.144976Elgin, IL71708917089852101201945493009DTDMV4MI5MT964
3201732338.095000NaN176NaN7239947230.083397Baton Rouge, LA8220052200503100020194549300VORTI31GZTJL534
3224732396.0325000NaN176NaN7439947270.451405Phoenix-Mesa-Chandler, AZ9040130401342264620194549300VORTI31GZTJL534
32287323102.0305000NaN476NaN7339947235.797665Fayetteville, NC6370933709397010120194549300VORTI31GZTJL534